Online optimal power flow with renewables

Optimal power flow (OPF) is a critical control task for reliable and efficient operation of power grids. Significant challenges are anticipated in the development of future power systems, as a substantial amount of inherently uncertain renewable resources are incorporated, imposing volatile dynamics to the grid. In this work, an online learning approach, which does not require elaborate models for uncertainty, yet is capable of providing a provable performance guarantee, is adopted to tackle the OPF with renewables in an online fashion. A two-stage procedure is considered, where the conventional generation level is committed before the renewable output is revealed, followed by spot market transactions to account for imbalance. Simulated tests with a 30-bus case show that, under high variability of renewables, the proposed hedging scheme beats a static alternative, which solves two OPF problems per time slot.